Spatial and temporal distribution and contamination assessment of heavy metal in Woji Creek
Bibliographic record
Abstract
Abstract Land use is one major factor that affects river water quality which is related to anthropogenic activities. Studies have shown that abandoned boats on watershed, petroleum and untreated wastewater from abattoirs can lead to anthropogenic pollution in surface waters. This study, therefore, was designed to assess spatial and temporal variation of selected heavy metals and level of pollution in Woji Creek. The study was carried out in the months of August, September and October 2018. Water samples were collected from five stations along the creek over a 3.2 km stretch. Water was collected to be analysed for heavy metals (Nickel, Cadmium, Copper, Lead and Iron). Results were subjected to ANOVA and heavy metal pollution index (HPI) was calculated using aquatic toxicity reference values (TRV) as threshold values. Heavy metal dominance in Woji was in the order of Pb > Ni > Fe > Cd > Cu. In the river, Ni had mean values ranging from 0.379 ± 0.259 mg l−1 in August to 0.545 ± 0.369 in October, while Pb with the highest concentration had mean values ranging from 0.229 ± 0.333 mg l−1 in October to 1.534 ± 0.103 mg l−1 in September. Concentrations of metals analysed were high than the TRV. Temporal analysis of HPI calculated for the study was above the critical heavy metal pollution index (100) (August = 329.358, September = 361.796, October = 112.715). A correlation was observed between heavy metals analysed during the study. Spatial analysis of HPI showed higher pollution level at Station 3 with the highest anthropogenic activity along the creek. Cu showed a negative correlation to other metals analysed. Sources of pollution on this creek was identified to be both natural and majorly anthropogenic sources. This study, therefore, points out the need for proper environmental management as regards commercial activities around the waterways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".